This paper proposes an adaptive grasshopper optimization algorithm (AGOA) for solving the optimal power flow (OPF) problem with the optimal incorporation of a center-node unified power flow controller (C-UPFC). The C-UPFC which is an advanced flexible AC transmission system (FACTS) device is inserted in series with a transmission line (TL) at its midpoint for providing the power flow control together with independent voltage control. The proposed AGOA is based on applying the Levy flight distribution and spiral path orientation of search agents to the traditional grasshopper optimization algorithm (GOA) to diminish the stagnation problem of the basic GOA at local optima and enhance its searching ability. Therefore, this AGOA technique is implemented for optimal sizing and siting of the C-UPFC on standard IEEE 30-bus and 57-bus systems as well as 26-bus system, and then compared with other well-known techniques to verify its effectiveness. To assess the installation of the C-UPFC in a power system, the optimal capacities and locations of the C-UPFC are determined for different objective functions, such as the fuel cost, fuel cost with a valve point loading effect (VPLE), piecewise cost and emission. Simulation results reveal that the proposed algorithm is more efficient and superior for OPF solution compared with the other algorithms reported in the literature. Furthermore, the optimal integration of the C-UPFC in the power system is considerably minimizing the power loss and improving the voltage profile.

The penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Determining realistic PEV load profiles is challenging due to the involvement of serval uncertainties and complex interdependencies among different factors and to date, there are no benchmark load profiles of PEVs. In this paper, realistic and ready-to-use load profiles for PEVs are developed by considering vehicle mobility, charging infrastructure, and the market share of PEVs. Firstly, the U.S. National Household Travel Survey (NHTS) data is filtered to remove vehicles with unrealistic, duplicate, and missing data. Secondly, a set of relevant parameters is extracted to estimate different features of PEVs, such as arrival time, departure time, and daily mileage. Then, all the commercially available PEVs are grouped into four clusters using the K-means algorithm. Finally, the per unit (per PEV) load profiles are estimated using the information of the available PEVs in the market, charging levels in the residential sector, and features extracted in the previous step. A large set of scenarios are considered for each PEV cluster in determining the load profiles. The pre-unit profiles estimated in this study are ready-to-use for researchers and planners in the PEV industry and are realistic due to consideration of different relevant factors and a large traveling database of vehicles. The developed per-unit load profiles are used to estimate and analyze the PEV load profiles of the top four countries with the highest penetration percentage of PEVs.

Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%.

The conventional neutral point clamped (NPC) multilevel inverter topology needs voltage balancing circuits to balance the dc-link capacitor, the number of component count is high and output voltage is half of the input voltage which increase the size of the source side dc/dc converter in PV applications. In this paper, a new topology of seven level neutral point clamped inverter is developed. The proposed inverter has a self-voltage boosting capability using a floating capacitor to boost the output voltage one and half of the input voltage. In this proposed topology, no additional sensors are required for the floating capacitor voltage stability, which results in minimizing the complexity of designing the inverter. The direct link between the neutral point and the mid-point of the dc-link capacitors significantly reduces the leakage current and common mode voltage in this inverter. A thorough comparison between the developed topology and recent suggested topologies is carried out which set the benchmark for the proposed one due to its lower switch count and higher voltage gain. The proposed topology is verified in simulation and prototype hardware model and results are discussed with dynamic load variations. The efficiency of the inverter is 97.1% @200W and low as 88.1 % @ inductive load. The voltage THD is 17.01% for simulation and 19.3% I for experimental is results.

This paper presents a new multilevel inverter topology with reduced active switches and total standing voltage. The proposed topology can generate a high number of voltage levels in the symmetric configuration. This topology intuitively generates positive and negative cycles without an additional H-bridge unit, which considerably reduces the total standing voltage of the inverter. A cascaded structure is developed from the proposed topology to create higher voltage levels. To show the novelty of the proposed topology, a thorough comparison between the available and the proposed topologies in terms of the number of switches, standing voltages, and dc-sources is presented. Furthermore, the power loss analysis is carried out for various load values. The feasibility of the proposed nine-level inverter is verified with simulation and experimental results.

Electrolytic capacitors have large capacity, low price, and fast charge/discharge characteristics. Therefore, they are widely used in various power conversion devices. These electrolytic capacitors are mainly used for temporary storage and voltage stabilization of DC energy and have recently been used in the renewable energy field for linking AC/DC voltage and buffering charge/discharge energy. However, electrolytic capacitors continue to be disadvantageous in their reliability due to their structural weaknesses due to the use of electrolytes and very thin oxide and dielectric materials. Most capacitors are considered a failure when the capacitance has changed by 25% of its initial value. Accurate and fast monitoring or estimation techniques are essential to be used with low cost and no extra hardware. In order to achieve these objectives, an online, reliable, and high-quality technique that continuously monitors the DC-link capacitor condition in a three-phase back-to-back converter is proposed. In this paper, the particle swarm optimization (PSO)-based support vector regression (PSO-SVR) approach is employed for online capacitance estimation based on sensing or deriving the capacitor current. Because the SVR performance alone severely depends on the tuning of its parameters, the PSO algorithm is used, which enables a fast online-based approach with high-parameter estimation accuracy. Experimental results are provided to verify the validity of the method.